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Related Concept Videos

Entropy02:39

Entropy

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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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Entropy01:18

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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
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Standard Entropy Change for a Reaction03:00

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Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
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Entropy and the Second Law of Thermodynamics01:20

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The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
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Related Experiment Video

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Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice
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Feature Selection Based on the Neighborhood Entropy.

Andrea Mariello, Roberto Battiti

    IEEE Transactions on Neural Networks and Learning Systems
    |July 12, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces neighborhood entropy, a new feature selection method that improves classification accuracy, especially for unbalanced and nonlinearly separable data. It offers a robust approach for offline analysis, prioritizing feature relevance and reducing uncertainty.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Pattern Recognition

    Background:

    • Mutual Information (MI) measures nonlinear feature-class relationships by quantifying uncertainty reduction.
    • Existing feature selection methods may struggle with highly unbalanced and nonlinearly separable datasets.

    Purpose of the Study:

    • Propose a novel feature selection measure, neighborhood entropy, related to MI.
    • Develop a filter method minimizing neighborhood entropy using a greedy approach.

    Main Methods:

    • Integration of sequential forward selection with approximated nearest-neighbors.
    • Application of locality-sensitive hashing for efficient computation.
    • Minimization of neighborhood entropy in a greedy procedure.

    Main Results:

    • Achieved higher classification accuracy compared to state-of-the-art algorithms.
    • Demonstrated superior performance on highly unbalanced and nonlinearly separable datasets.
    • Feature selection order improved accuracy with fewer features.

    Conclusions:

    • The proposed neighborhood entropy method offers a robust and effective feature selection technique.
    • Effective for offline scenarios requiring high accuracy, robustness to noise, and class imbalance.
    • Provides a superior feature ordering for enhanced model performance.